The Missing Middle (Solving For Marketing's Operational Layer - Part 2)

Part 2 of 2: We continue our series on the evolution of performance marketing. In this article, we explore the solution to the divide between data teams and marketers and what it means for the future of performance marketing (Introducing the idea of the Pragmatic Analytics Stack).

The modern marketing technology landscape has evolved to serve two distinct layers extremely well. At the top, BI dashboards and data visualisation tools provide executives with the strategic views they need. At the bottom, sophisticated platform interfaces from Google, Facebook, and others give marketers powerful execution capabilities. (Read Part 1 to find out how the performance marketing landscape developed)

However, between these two layers lies a middle layer. We call this the operational layer. In performance marketing, marketers are often left to manage this critical gap, caught between management and execution, without the tools they actually need. And, at a certain point, the sheer quantity of data makes life very challenging for marketers. (See here for why marketers keep going back to spreadsheets)

This gap is particularly striking when we look at how other domains have solved similar challenges. Product teams, for instance, once faced the same disconnect between execution data and strategic insight. Their solution? Specialised vertical tools like Amplitude and Mixpanel that were built specifically for product analytics. These platforms didn't try to be general-purpose BI tools—they were purposefully built to serve product teams' unique needs, speaking their language and solving their specific problems. The result has been transformative for product development, enabling product teams to move faster and make better decisions without constantly requiring engineering support.

Performance marketing teams need and deserve the same kind of specialised solution. Today, they're caught between management and execution layers, forced to improvise with a combination of tools that weren't built for their needs. As we showed in Part 1, it's not that companies haven't tried to bridge this gap. They've invested millions in both their management and execution layers. But without specialised tools for this operational middle layer, marketing teams are forced to cobble together solutions from spreadsheets, manual processes, and workarounds.

The result is a peculiar paradox: despite having access to sophisticated BI dashboards and powerful campaign tools, marketing teams still spend countless hours manually translating between these layers. They export data from execution platforms, manipulate it in spreadsheets, and then try to map it to the formats and metrics needed for management reporting. This isn't just inefficient—it's a fundamental bottleneck in how modern marketing organisations operate.

What's missing isn't better dashboards or more sophisticated campaign tools. What's missing is a specialised solution for this operational layer—a vertical tool designed specifically for the complex task of bridging execution and strategy in performance marketing, just as Amplitude and Mixpanel did for product teams.

And so, Clarisights

This operational intelligence gap isn't just theoretical. It's a problem experienced by performance marketers on a daily basis. While most technology providers focused on either improving executive dashboards or enhancing campaign execution tools, we saw the critical need for something different: a specialised solution for the operational middle layer where marketing teams actually spend most of their time.

This realisation led to the creation of Clarisights. Unlike general-purpose BI tools that serve the management layer, or marketing platforms that focus on campaign execution, Clarisights was conceived as the first vertical solution specifically for marketing's operational layer. We approached this as an engineering challenge first, building our own HTAP database engine, MorselDB, and orchestrator, Mozek, designed to handle the unique velocity and complexity of marketing data flowing between execution and management layers. Ultimately, we realised we had to build an ETL pipeline from the ground up, that was designed specifically for the unique properties and use cases of performance marketing data.

Clarisights isn't just another dashboard tool or campaign platform; it's the missing piece that makes both marketing teams and data teams more effective.

The result is a platform that finally bridges the gap between strategic dashboards and tactical execution. For marketing teams, it provides the speed and flexibility they need to connect campaign insights with business strategy—all without writing SQL or waiting for engineering support. For data teams, it delivers the governance and reliability they require, built on solid engineering principles rather than quick fixes. Clarisights isn't just another dashboard tool or campaign platform; it's the missing piece that makes both marketing teams and data teams more effective.

This engineering-first approach to solving the operational layer challenge has produced something remarkable: a platform that speaks both languages. It can translate granular campaign metrics into strategic insights and break down high-level goals into actionable campaign decisions. The platform embodies our belief that the best solutions come from deep collaboration between marketing and engineering expertise. This is what we mean when we say "built for marketers by data engineers"—it's about bringing together deep technical expertise and real-world marketing experience to solve the operational challenges that have plagued our industry for too long.

Building an effective operational layer isn't just about implementing new tools—it requires a fundamental shift in how marketing and data teams work together.

These teams aren't service providers to each other, but partners in creating business value. This partnership needs to be reflected in what we call the pragmatic analytics stack, which recognises that different layers of the organisation have different needs and provides specialised tools for each.

Successfully implementing this stack requires clear separation of concerns. Data teams should own data governance and infrastructure—deciding what constitutes revenue, how attribution works, and maintaining data quality. Marketing teams should own campaign strategy and optimisation—deciding what metrics to track, what insights to surface, and how to act on them. The operational layer provides the tools and frameworks that enable both teams to focus on what they do best.

Companies need to rethink their reporting cadences, their decision-making processes, and even their team structures. The traditional linear model of weekly review meetings and quarterly planning cycles must give way to a more cyclical, dynamic data-driven approach where insights flow continuously between execution and management layers. This evolution from linear to cyclical operations is crucial for keeping pace with modern marketing channels—a topic we'll explore in depth in a future piece.

What's Next For Performance Marketing

We are in the era of AI-generated creative and automated campaign optimisation, and so the need for operational intelligence grows everyday. The winners will be companies that can effectively bridge the gap between automated execution and strategic management.

The future of performance marketing isn't about more automation or better dashboards. It's about building an operational intelligence layer that makes both more effective. After watching this industry evolve for over two decades, we are convinced this is the missing piece we've been looking for.

This article draws from over two decades of experience in performance marketing, witnessing and participating in every major shift in the industry. Its author was one of the first advertisers on "The Facebook" in 2005, when the platform still ran Google AdSense ads. Since then, he has managed campaigns across every major digital channel, built and led marketing teams at rapidly scaling companies, and experienced firsthand the evolution from manual campaign management to today's AI-driven landscape.